%!TEX root = /Users/dejean/Documents/ITU/2aar/MAIG-E2011/exam_project/report/simmar_report.tex
\section{Related work}
Interestingly, one approach to a well-performing AI is to not use advanced AI at all. The A* algorithm works very well due to the deterministic nature of events in Mario, as demonstrated by Robin Baumgarten's submission for the 2009 Mario AI Competetion \cite{baumgarten}. 

This approach will still require the game state to be abstracted somewhat, as the search space would otherwise be prohibitively large. The methods are still helpful examples of how the inclusion of planning could be handled for an advanced AI agent.

Galactic Arms Race \cite{gar} (GAR) is a powerful example of NEAT used in a game. In GAR, the method is used to evolve networks that control the visual appearance of particle effects, with the goal of evolving effects that look good. 

The ability of the topology to evolve as well is they key in this case as it allows the effect to not only span the possible permutations of weights, but also the set of allowed topologies. 

Besides that, NEAT has proven itself to be an efficient NeuroEvolution method in general, on tests such as the pole balancing challenge.

To the authors' knowledge, the only documented attempt at using Learning Classifier Systems as the basis for a Mario Agent is the REALM agent, submitted for the 2010 Mario AI Competetion \cite{realm}.